Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/sawrus/agent-guides/data-modelingnpx skills add sawrus/agent-guides --skill data-modelinggit clone --depth 1 https://github.com/sawrus/agent-guidesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sawrus/agent-guides/data-modeling)<a href="https://agentmods.dev/skills/sawrus/agent-guides/data-modeling"><img src="https://agentmods.dev/badge/skills/sawrus/agent-guides/data-modeling.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00361 |
| Opus 5 | $0.00000 | $0.00180 |
| Sonnet 5 | $0.00000 | $0.00072 |
| Haiku 4.5 | $0.00000 | $0.00036 |
Grade A, and why
data-modeling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Skill: Data Modeling
When to load
When designing a warehouse schema, choosing between modeling approaches, or refactoring an existing model.
Approach Decision Tree
Primary consumer?
→ BI tools + analysts: Kimball (dimensional)
→ Data science + ML: Wide denormalized tables
→ Multiple teams: Data Vault
History tracking?
→ Never/rarely: Type 1 SCD (overwrite)
→ Track current + previous: Type 2 SCD (versioned rows)
Scale?
→ < 100 GB: Simple star schema
→ 100 GB – 10 TB: Partitioned star schema
→ > 10 TB: Data Vault or Lakehouse (Delta/Iceberg)
dbt Layering (Medallion Architecture)
sources/ ← Raw data as-is from source systems
staging/ ← Cleaned, renamed, typed. 1:1 with source tables.
intermediate/ ← Business logic, joins. Not exposed directly.
marts/ ← Dimensional models for specific use cases
├── core/ ← fct_*, dim_*
└── finance/ ← rpt_*
Fact Table Template
CREATE TABLE fct_orders (
order_key BIGINT PRIMARY KEY, -- surrogate key
order_id VARCHAR NOT NULL, -- natural key
user_key BIGINT REFERENCES dim_users(user_key),
date_key INT REFERENCES dim_date(date_key),
quantity INT NOT NULL,
total_amount NUMERIC(10,2) NOT NULL,
loaded_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
source_system VARCHAR DEFAULT 'orders-api'
);
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 50 lines · 0 tokens per session scan A 4cbf6348b337
data-modeling is a skill published in the GitHub repository sawrus/agent-guides (17 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 361 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
advanced-evaluation
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
00-andruia-consultant
Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
coupling-analysis
Analyzes coupling between modules using the three-dimensional model (strength, distance, volatility) from "Balancing Coupling in Software Design". Use when asking "are these modules too coupled?", "show me dependencies", "analyze integration quality", "which modules should I decouple?", "coupling report", or…
codenavi
Your pathfinder for navigating unknown codebases. Investigates with precision, implements surgically, and never assumes — if it doesn't know, it says so. Maintains a .notebook/ knowledge base that grows across sessions, turning every discovery into lasting intelligence. Summons available skills, MCPs, and docs when…
coding-guidelines
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, modifying, or reviewing code — implementation tasks, code changes, refactoring, bug fixes, or feature development. Do NOT use for architecture design, documentation, or non-code tasks.
architect
Architectural decision advisor for cloud, data, and SaaS distributed systems. Use when the user asks "should we use X or Y", weighs a tradeoff, or needs a decision matrix, scaling analysis, cost-at-scale review, data-tier design, multi-tenancy model, or API/resilience guidance. For full technical blueprints, use…